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    "## Models\n",
    "\n",
    "Below are listed the proposed demo notebooks for each implemented model. Feel free to open them directly on colab using the colab badge. Hope you'll enjoy them!\n",
    "\n",
    "Do not hesitate to checkout the [github repo](https://github.com/clementchadebec/benchmark_VAE) and [documentation](https://pythae.readthedocs.io/en/latest/index.html) for further information.\n",
    "\n",
    "\n",
    "| Paper |               Models               |                                                                                    Training example                                                                                    |                     Paper link                   |                           Official Implementation                          |\n",
    "|:----------------------------------|:----------------------------------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:--------------------------------------------:|:--------------------------------------------------------------------------:|\n",
    "| | Autoencoder (AE)                   | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/models_training/ae_training.ipynb) |                                              |                                                                            |\n",
    "| Auto-Encoding Variational Bayes | Variational Autoencoder (VAE)      | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/models_training/vae_training.ipynb) | [link](https://arxiv.org/abs/1312.6114)  |\n",
    "| β-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework | β-Variational Autoencoder (BetaVAE) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/models_training/beta_vae_training.ipynb) | [link](https://openreview.net/pdf?id=Sy2fzU9gl)  |   \n",
    "Variational Inference with Normalizing Flows | VAE with Linear Normalizing Flows (VAE_LinNF) |  [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/models_training/vae_lin_nf_training.ipynb) | [link](https://arxiv.org/abs/1505.05770) |                                                                            |\n",
    "Improved Variational Inference with Inverse Autoregressive Flow | VAE with Inverse Autoregressive Flows (VAE_IAF) |  [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/models_training/vae_iaf_training.ipynb) | [link](https://arxiv.org/abs/1606.04934) |  [link](https://github.com/openai/iaf)                                  |\n",
    "| Understanding disentangling in β-VAE | Disentangled β-VAE (DisentangledBetaVAE) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/models_training/disentangled_beta_vae_training.ipynb) | [link](https://arxiv.org/abs/1804.03599)  | \n",
    "| Disentangling by Factorising  | Factor-VAE (FactorVAE) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/models_training/factor_vae_training.ipynb) | [link](https://arxiv.org/abs/1802.05983)  |  \n",
    "| Isolating Sources of Disentanglement in Variational Autoencoders  | β-TC-VAE (BetaTCVAE) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/models_training/beta_tc_vae_training.ipynb) | [link](https://arxiv.org/abs/1802.04942)  |  [link](https://github.com/rtqichen/beta-tcvae)\n",
    "| Importance Weighted Autoencoders | Importance Weighted Autoencoder (IWAE) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/models_training/iwae_training.ipynb) | [link](https://arxiv.org/abs/1509.00519v4)  | [link](https://github.com/yburda/iwae)   \n",
    "| Tighter Variational Bounds are Not Necessarily Better | Partially/Multiply/Combination Importance Weighted Autoencoder (PIWAE/MIWAE/CIWAE) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/models_training/miwae_training.ipynb) | [link](https://arxiv.org/abs/1802.04537)  |                                                                             |\n",
    "| Learning to Generate Images with Perceptual Similarity Metrics | VAE with perceptual metric similarity (MSSSIM_VAE)  | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/models_training/ms_ssim_vae_training.ipynb) | [link](https://arxiv.org/abs/1511.06409)  |\n",
    "| Wasserstein Auto-Encoders | Wasserstein Autoencoder (WAE)      | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/models_training/wae_training.ipynb) | [link](https://arxiv.org/abs/1711.01558) | [link](https://github.com/tolstikhin/wae)                                  |\n",
    "| InfoVAE: Information Maximizing Variational Autoencoders | Info Variational Autoencoder (INFOVAE_MMD)      | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/models_training/info_vae_training.ipynb) | [link](https://arxiv.org/abs/1706.02262) |                                   |\n",
    "| VAE with a VampPrior | VAMP Autoencoder (VAMP)            | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/models_training/vamp_training.ipynb) | [link](https://arxiv.org/abs/1705.07120) | [link](https://github.com/jmtomczak/vae_vampprior)                         |\n",
    "| Hyperspherical Variational Auto-Encoders | Hyperspherical VAE (SVAE) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/models_training/svae_training.ipynb) | [link](https://arxiv.org/abs/1804.00891) | [link](https://github.com/nicola-decao/s-vae-pytorch)                         |\n",
    "| Continuous Hierarchical Representations with Poincaré Variational Auto-Encoders | Poincaré Disk VAE (PoincareVAE) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/models_training/pvae_training.ipynb) | [link](https://arxiv.org/abs/1901.06033) | [link](https://github.com/emilemathieu/pvae)                         |\n",
    "| Adversarial Autoencoders | Adversarial Autoencoder (Adversarial_AE)                   | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/models_training/adversarial_ae_training.ipynb) | [link](https://arxiv.org/abs/1511.05644)\n",
    "| Autoencoding beyond pixels using a learned similarity metric | Variational Autoencoder GAN (VAEGAN) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/models_training/vaegan_training.ipynb) | [link](https://arxiv.org/abs/1512.09300) | [link](https://github.com/andersbll/autoencoding_beyond_pixels)\n",
    "| Neural Discrete Representation Learning | Vector Quantized VAE (VQVAE) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/models_training/vqvae_training.ipynb) | [link](https://arxiv.org/abs/1711.00937) | [link](https://github.com/deepmind/sonnet/blob/v2/sonnet)\n",
    "| Hamiltonian Variational Auto-Encoder | Hamiltonian VAE (HVAE)             | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/models_training/hvae_training.ipynb) | [link](https://arxiv.org/abs/1805.11328) | [link](https://github.com/anthonycaterini/hvae-nips)                       |\n",
    "| From Variational to Deterministic Autoencoders | Regularized AE with L2 decoder param (RAE_L2) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/models_training/rae_l2_training.ipynb) | [link](https://arxiv.org/abs/1903.12436) | [link](https://github.com/ParthaEth/Regularized_autoencoders-RAE-/tree/master/) |\n",
    "| From Variational to Deterministic Autoencoders | Regularized AE with gradient penalty (RAE_GP) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/models_training/rae_gp_training.ipynb) | [link](https://arxiv.org/abs/1903.12436) | [link](https://github.com/ParthaEth/Regularized_autoencoders-RAE-/tree/master/) |\n",
    "| Data Augmentation in High Dimensional Low Sample Size Setting Using a Geometry-Based Variational Autoencoder | Riemannian Hamiltonian VAE (RHVAE) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/models_training/rhvae_training.ipynb) | [link](https://arxiv.org/abs/2105.00026) | [link](https://github.com/clementchadebec/pyraug)|\n"
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